Highly parallel VLSI architectures for linear convolution

Abstract

This paper presents highly parallel VLSI structures for linear convolution. Our methodology implements Toom's algorithm and is based on mapping a modified version of the tensor product factorization proposed by Granata et. al. [4]. The resulting networks have very simple structure, highly regular topology, and use simple bit-serial devices. Additionally, the proposed networks have very small depth and contain only a single stage of multipliers, while all other stages contain adders only.

abstract = "This paper presents highly parallel VLSI structures for linear convolution. Our methodology implements Toom's algorithm and is based on mapping a modified version of the tensor product factorization proposed by Granata et. al. [4]. The resulting networks have very simple structure, highly regular topology, and use simple bit-serial devices. Additionally, the proposed networks have very small depth and contain only a single stage of multipliers, while all other stages contain adders only.",

N2 - This paper presents highly parallel VLSI structures for linear convolution. Our methodology implements Toom's algorithm and is based on mapping a modified version of the tensor product factorization proposed by Granata et. al. [4]. The resulting networks have very simple structure, highly regular topology, and use simple bit-serial devices. Additionally, the proposed networks have very small depth and contain only a single stage of multipliers, while all other stages contain adders only.

AB - This paper presents highly parallel VLSI structures for linear convolution. Our methodology implements Toom's algorithm and is based on mapping a modified version of the tensor product factorization proposed by Granata et. al. [4]. The resulting networks have very simple structure, highly regular topology, and use simple bit-serial devices. Additionally, the proposed networks have very small depth and contain only a single stage of multipliers, while all other stages contain adders only.